An intriguing problem in healthcare analytics, particularly in photoplethysmogram (PPG) signals based proactive cardiac health monitoring, is the frequent presence of corruption in PPG signal from multiple noise sources like motion artifacts, ambient noise. Data preprocessing through denoising results in improved interpretation of the cardiovascular systems and removal of false alarms. In fact, the occurrence of high false alarms (precisely false positives due to the presence of noise) leads to ‘alarm fatigue’. In contrary, high false negatives would be fatal to the patients.
In all practicality, it is infeasible to capture ECG or ABP signals without using extra sensors or through invasive procedures. It is observed that majority of computational techniques do not find widespread clinical use because of their lack of capability in reducing the artifacts and other noises. Prior art illustrates 80 different artifact detection techniques and it is perceived that most of the techniques are highly explicit, hard coded to suit for certain device settings, usage and thus limiting their applicability in almost all practical purposes. Current trend is towards multi-signal corruption analysis and detection. The underlying assumption is that all of the cardiological signals like viz. PPG, ECG, ABP cannot be corrupted simultaneously while source of corruption is independent. Stand-alone method of PPG signal denoising and quality assessment is presented in prior art, which employs computationally heavy machine learning algorithms and multivariate ‘voting’ threshold mechanism. Such schemes are not suitable for smartphones to deliver real-time performance. Another problem is the single-stage error detection is itself erroneous and prone to have high false negatives.
Thereby, removing corruption in photoplethysmogram (PPG) signals for monitoring cardiac health of patient's negatives is still considered to be one of the biggest challenges of the technical domain.